TY - JOUR AU - Chen, Zhu-Mu AU - Yeh, Mi-Yen AU - Kuo, Tei-Wei PY - 2021/05/18 Y2 - 2024/03/28 TI - PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 5 SE - AAAI Technical Track on Data Mining and Knowledge Management DO - 10.1609/aaai.v35i5.16522 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16522 SP - 4019-4026 AB - In this paper, we study the problem of embedding uncertain knowledge graphs, where each relation between entities is associated with a confidence score. Observing the existing embedding methods may discard the uncertainty information, only incorporate a specific type of score function, or cause many false-negative samples in the training, we propose the PASSLEAF framework to solve the above issues. PASSLEAF consists of two parts, one is a model that can incorporate different types of scoring functions to predict the relation confidence scores and the other is the semi-supervised learning model by exploiting both positive and negative samples associated with the estimated confidence scores. Furthermore, PASSLEAF leverages a sample pool as a relay of generated samples to further augment the semi-supervised learning. Experiment results show that our proposed framework can learn better embedding in terms of having higher accuracy in both the confidence score prediction and tail entity prediction. ER -